{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T22:01:04Z","timestamp":1790373664677,"version":"4.1.0"},"reference-count":66,"publisher":"Oxford University Press (OUP)","license":[{"start":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T00:00:00Z","timestamp":1790294400000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"name":"National Institute of Child Health and Human Development of the National Institutes of Health","award":["F31HD114398"],"award-info":[{"award-number":["F31HD114398"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>To externally validate a pediatric surgical site infection (SSI) prediction model, assess institutional and temporal transportability, and evaluate recalibration strategies to support adaptation of registry-derived predictions for clinical decision support (CDS).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods<\/jats:title>\n                    <jats:p>An elastic-net logistic regression model developed using a national pediatric surgical registry (2012-2022) was validated in an institutional electronic health record (EHR)-derived cohort (10\u2009450 procedures) and in a newly released, temporally distinct cohort from the same registry (301\u2009999 procedures). We assessed discrimination, calibration, and overall performance, used a closed testing procedure to guide recalibration, and evaluated clinical utility using decision curve analysis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Performance was preserved in the registry cohort, with c-statistic 0.75, Brier score 0.03, calibration slope 1.00, and observed\/expected (O\/E) ratio 1.01. Direct application to the institutional EHR cohort produced inflated predicted risk and poor performance, with c-statistic 0.60, Brier score 0.07, calibration slope 0.15, and O\/E ratio 0.25. After omitting open-ended diagnosis-code indicators, performance improved, with c-statistic 0.70, Brier score 0.03, calibration slope 0.81, and O\/E ratio 1.35. Logistic recalibration restored calibration slope and O\/E ratio to 1.00. Decision curve analysis showed positive net benefit across prespecified thresholds.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>These findings highlight a key challenge for clinical translation: models that perform well under internal or registry-based validation may not be directly transportable to real-world EHR environments. Predictor misalignment\u2014differences in how model predictors were captured and measured across registry and EHR data\u2014emerged as a key barrier to direct institutional deployment, underscoring the importance of implementation-aware external validation before clinical use.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Addressing diagnosis-code predictor misalignment and applying parsimonious recalibration supported institutional adaptation of a pediatric SSI prediction model, providing a pragmatic pathway for translating registry-derived models into real-world clinical practice. This work sets the foundation for prospective evaluation of EHR-embedded CDS to enable individualized SSI risk estimation at the point of care.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/jamia\/ocag162","type":"journal-article","created":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T11:16:42Z","timestamp":1788520602000},"source":"Crossref","is-referenced-by-count":0,"title":["Transporting a pediatric surgical site infection prediction model to the electronic health record: external validation, harmonization, and recalibration"],"prefix":"10.1093","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7190-4512","authenticated-orcid":false,"given":"Carrie T","family":"Chan","sequence":"first","affiliation":[{"name":"Department of Epidemiology & Biostatistics, University of California , San Francisco, CA 94158,","place":["United States"]},{"name":"Stanford Medicine Children\u2019s Health , Palo Alto, CA 94304,","place":["United 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